000 04088nam a22005415i 4500
001 978-3-319-02472-1
003 DE-He213
005 20200421112228.0
007 cr nn 008mamaa
008 131112s2014 gw | s |||| 0|eng d
020 _a9783319024721
_9978-3-319-02472-1
024 7 _a10.1007/978-3-319-02472-1
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aPonce-Espinosa, Hiram.
_eauthor.
245 1 0 _aArtificial Organic Networks
_h[electronic resource] :
_bArtificial Intelligence Based on Carbon Networks /
_cby Hiram Ponce-Espinosa, Pedro Ponce-Cruz, Arturo Molina.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXII, 228 p. 192 illus., 56 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v521
505 0 _aIntroduction to Modeling Problems -- Chemical Organic Compounds -- Artificial Organic Networks -- Artificial Hydrocarbon Networks -- Enhancements of Artificial Hydrocarbon Networks -- Notes on Modeling Problems Using Artificial Hydrocarbon Networks -- Applications of Artificial Hydrocarbon Networks.-Appendices.
520 _aThis monograph describes the synthesis and use of biologically-inspired artificial hydrocarbon networks (AHNs) for approximation models associated with machine learning and a novel computational algorithm with which to exploit them. The reader is first introduced to various kinds of algorithms designed to deal with approximation problems and then, via some conventional ideas of organic chemistry, to the creation and characterization of artificial organic networks and AHNs in particular. The advantages of using organic networks are discussed with the rules to be followed to adapt the network to its objectives. Graph theory is used as the basis of the necessary formalism. Simulated and experimental examples of the use of fuzzy logic and genetic algorithms with organic neural networks are presented and a number of modeling problems suitable for treatment by AHNs are described: �        approximation; �        inference; �        clustering; �        control; �        classification; and �        audio-signal filtering. The text finishes with a consideration of directions in which AHNs  could be implemented and developed in future. A complete LabVIEW™ toolkit, downloadable from the book's page at springer.com enables readers to design and implement organic neural networks of their own. The novel approach to creating networks suitable for machine learning systems demonstrated in Artificial Organic Networks will be of interest to academic researchers and graduate students working in areas associated with computational intelligence, intelligent control, systems approximation and complex networks.
650 0 _aEngineering.
650 0 _aBiochemical engineering.
650 0 _aArtificial intelligence.
650 0 _aComputer simulation.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aBiochemical Engineering.
650 2 4 _aSimulation and Modeling.
700 1 _aPonce-Cruz, Pedro.
_eauthor.
700 1 _aMolina, Arturo.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319024714
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v521
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-02472-1
912 _aZDB-2-ENG
942 _cEBK
999 _c57792
_d57792